Posture sensor automatic calibration

ABSTRACT

A system and method automatically calibrate a posture sensor, such as by detecting a walking state or a posture change. For example, a three-axis accelerometer can be used to detect a patient&#39;s activity or posture. This information can be used to automatically calibrate subsequent posture or acceleration data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims the benefit of priorityunder 35 U.S.C. §120 to U.S. patent application Ser. No. 12/425,195,filed on Apr. 16, 2009, now U.S. Pat. No. 8,165,840, which claims thebenefit of priority of expired U.S. Provisional Application Ser. No.61/060,987, filed on Jun. 12, 2008, under 35 U.S.C. §119(e), which ishereby incorporated by reference.

BACKGROUND

Cardiac rhythm management devices are used to monitor and treat cardiacpatients. Acceleration is one possible input for a cardiac rhythmmanagement device.

OVERVIEW

This document describes, among other things, a system and method forautomatically calibrating a posture sensor, such as by detecting awalking state or a posture change. For example, a three-axisaccelerometer can be used to detect a patient's activity or posture.This information can be used to automatically calibrate subsequentposture or acceleration data.

Example 1 describes a method. In this example, the method comprisessensing an acceleration signal from a patient using an accelerometer;detecting a walking state using the acceleration signal, the detectingthe walking state including: extracting a characteristic of an ACcomponent of the acceleration signal; performing a comparison betweenthe characteristic and a template; and using the comparison todistinguish the walking state from a non-walking activity state; andusing an orientation of the accelerometer in the walking state toperform a posture calibration of the acceleration signal of theaccelerometer.

In Example 2, the method of Example 1 optionally comprises sensing anacceleration signal from a patient using an accelerometer by sensing anacceleration signal from an implantable accelerometer when implanted inthe patient.

In Example 3, the method of one or any combination of Examples 1-2optionally comprises computing a DC component of the accelerationsignal; determining a gravity vector using the DC component of theacceleration signal; computing a parallel component of the accelerationsignal, the parallel component being in the direction of the gravityvector; computing an orthogonal component of the acceleration signal,the orthogonal component being in a direction orthogonal to the gravityvector; wherein the performing the comparison comprises comparing theparallel component to a parallel template and comparing the orthogonalcomponent to an orthogonal template; and wherein the using thecomparison to distinguish the walking state from the non-walkingactivity state comprises using the computed parallel component of theacceleration signal and using the computed orthogonal component of theacceleration signal.

In Example 4, the method of one or any combination of Examples 1-3optionally comprises using the comparison to distinguish the walkingstate from the non-walking activity state by comparing the parallelcomparison to a first threshold value and comparing the orthogonalcomparison to a second threshold value.

In Example 5, the method of one or any combination of Examples 1-4optionally comprises computing the DC component of the accelerationsignal by computing a DC component of the acceleration signal in each oforthogonal x, y, and z directions of the accelerometer; and determininga gravity vector by determining a three dimensional gravity vector usingthe DC component of the acceleration signal in each of x, y, and zdirections of the accelerometer.

In Example 6, the method of one or any combination of Examples 1-5optionally comprises using the comparison to distinguish the walkingstate from the non-walking activity state by: determining a centraltendency of the gravity vectors; comparing the central tendency of thegravity vectors to a previously-determined central tendency of thegravity vectors; and determining whether the gravity vectors areconsistent using a difference between the central tendency of thegravity vectors and the previously-determined central tendency of thegravity vectors.

In Example 7, the method of one or any combination of Examples 1-6optionally comprises performing a comparison between the characteristicand a template by performing the comparison between at least one of: afrequency-domain characteristic and a frequency-domain template; atime-domain characteristic and a time-domain template; and atime-frequency-domain characteristic and a time-frequency-domaintemplate.

In Example 8, the method of one or any combination of Examples 1-7optionally comprises using the comparison to distinguish the walkingstate from a non-walking activity state by comparing the comparison to athreshold value.

Example 9 is a method that comprises sensing an acceleration signal froma patient using an accelerometer; sensing a first steady state posturefrom the acceleration signal; calculating a first gravity vectororientation of plurality of gravity vectors detected from theacceleration signal during a first time period and in the first steadystate posture; detecting a second gravity vector orientation thatdiffers from the first gravity vector orientation by at least athreshold value; detecting a second steady state posture associated withthe second gravity vector orientation; and upon detecting the secondsteady state posture associated with the second gravity vectororientation, automatically performing a calibration using a plurality ofgravity vectors associated with only one of either the first or secondsteady state postures.

In Example 10, the method of Example 9 optionally comprises sensing anacceleration signal from a patient using an accelerometer by sensing anacceleration signal from an implantable accelerometer when implanted inthe patient.

In Example 11, the method of one or any combination of Examples 9-10optionally comprises sensing an acceleration signal from a patient usingan accelerometer by determining a DC component of the accelerationsignal; and detecting a second steady state posture associated with thesecond gravity vector orientation comprises detecting a change inorientation between the first gravity vector orientation and the secondgravity vector orientation, the change in orientation being above aspecified threshold value for at least a specified second time period.

In Example 12, the method of one or any combination of Examples 9-11optionally comprises calculating a first gravity vector orientation ofplurality of gravity vectors detected from the acceleration signalduring a first time period and in the first steady state posture bydetermining a consistency of the first gravity vector orientation duringthe first time period; and detecting a second steady state postureassociated with the second gravity vector orientation comprisesdetermining a consistency of the second gravity vector orientationduring the second time period; and when the first and second gravityvector orientations are deemed consistent, using a central tendency ofone of the consistent first and second gravity vector orientations toperform a posture calibration of the acceleration signal of theaccelerometer.

In Example 13, the method of one or any combination of Examples 9-12optionally comprises sensing an acceleration signal from a patient bycomputing a DC component of the acceleration signal in each oforthogonal x, y, and z directions of the accelerometer.

In Example 14, the method of one or any combination of Examples 9-13optionally comprises determining a consistency of the first or secondgravity vector orientations during the first time period by: determininga central tendency of the gravity vectors; comparing the centraltendency of the gravity vectors to a previously-determined centraltendency of the gravity vectors; and determining whether the gravityvectors are consistent using a difference between the central tendencyof the gravity vectors and the previously-determined central tendency ofthe gravity vectors.

In Example 15, the method of one or any combination of Examples 9-14optionally comprises determining a consistency of the first or secondgravity vector orientations during the first time period by: determininga dispersion of the gravity vectors; and determining whether the gravityvectors are consistent using a comparison of the dispersion to aspecified dispersion threshold value.

In Example 16, the method of one or any combination of Examples 9-15optionally comprises computing the specified dispersion threshold valueusing a previously-determined dispersion of the gravity vectors.

In Example 17, the method of one or any combination of Examples 9-16optionally comprises identifying a posture during the first or secondtime period.

In Example 18, the method of one or any combination of Examples 9-17optionally comprises identifying the posture during the first or secondtime period by using at least one of: a time of day for the identifying;a patient activity level for the identifying; and a physiologic sensor,of a different type than the accelerometer, for the identifying.

In Example 19, the method of one or any combination of Examples 9-18optionally comprises determining the consistency of the first or secondgravity vector orientations and, when the first or second gravity vectororientations are deemed inconsistent, performing the posture calibrationwithout using the inconsistent gravity vectors.

Example 20 includes a device comprising an accelerometer, configured tosense an acceleration signal from a patient; and a signal processorcircuit, coupled to the accelerometer, the signal processor circuitcomprising or coupled to: a walking state detection circuit, configuredto detect a walking state of the patient using the acceleration signal,the walking state detection circuit comprising: a highpass filtercircuit, configured to extract an AC component of the accelerationsignal, and further configured to extract a characteristic of the ACcomponent of the acceleration signal; a stored template; a comparisoncircuit, coupled to the filter circuit and the template, the comparisoncircuit configured to perform a comparison between the characteristicand the template to distinguish the walking state from a non-walkingactivity state; and a posture calibration circuit, coupled to thecomparison circuit, the posture calibration circuit configured to usethe orientation of the accelerometer in the walking state to perform aposture calibration of the acceleration signal of the accelerometer.

In Example 21, the device of Example 20 optionally comprises anaccelerometer that is implantable.

In Example 22, the device of one or any combination of Examples 20-21optionally comprises a lowpass filter circuit, configured to compute aDC component of the acceleration signal; and the signal processorcircuit configured to: determine a gravity vector using the DC componentof the acceleration signal; compute a parallel component of theacceleration signal, the parallel component being in the direction ofthe gravity vector; compute an orthogonal component of the accelerationsignal, the orthogonal component being in a direction orthogonal to thegravity vector; compare the parallel component to a parallel templateand compare the orthogonal component to an orthogonal template; anddistinguish the walking state from the non-walking activity state usingthe computed parallel component of the acceleration signal and using thecomputed orthogonal component of the acceleration signal.

In Example 23, the device of one or any combination of Examples 20-22optionally comprises the signal processor circuit configured todistinguish the walking state from the non-walking activity state by:determining a central tendency of the gravity vectors; comparing thecentral tendency of the gravity vectors to a previously-determinedcentral tendency of the gravity vectors; and determining whether thegravity vectors are consistent using a difference between the centraltendency of the gravity vectors and the previously-determined centraltendency of the gravity vectors.

In Example 24, the device of one or any combination of Examples 20-23optionally comprises a stored domain template, the domain being at leastone of frequency, time, and time-frequency; and the comparison circuitconfigured to perform a comparison between the characteristic and thedomain template, wherein the characteristic comprises a domaincharacteristic.

Example 25 includes a device comprising an accelerometer, configured tosense an acceleration signal from a patient; and a signal processorcircuit, coupled to the accelerometer, the signal processor circuitconfigured to: sense a first steady state posture from the accelerationsignal; calculate a first gravity vector orientation of plurality ofgravity vectors detected from the acceleration signal during a firsttime period and in the first steady state posture; detect a secondgravity vector orientation that differs from the first gravity vectororientation by at least a threshold value; detect a second steady stateposture associated with the second gravity vector orientation; and upondetecting the second steady state posture associated with the secondgravity vector orientation, automatically perform a calibration using aplurality of gravity vectors associated with only one of either thefirst or second steady state postures.

In Example 26, the device of Example 25 optionally comprises anaccelerometer that is implantable.

In Example 27, the device of one or any combination of Examples 25-26optionally comprises a lowpass filter circuit, coupled to theaccelerometer, configured to use the acceleration signal from thepatient to determine a DC component of the acceleration; and the signalprocessor circuit configured to detect a second steady state postureassociated with the second gravity vector orientation by detecting achange in orientation between the first gravity vector orientation andthe second gravity vector orientation, the change in orientation beingabove a specified threshold value for at least a specified second timeperiod.

In Example 28, the device of one or any combination of Examples 25-27optionally comprises the signal processor circuit is configured to:determine a consistency of the first gravity vector orientation duringthe first time period; determine a consistency of the second gravityvector orientation during the second time period; and when the first andsecond gravity vector orientations are deemed consistent, use a centraltendency of one of the consistent first and second gravity vectororientations to perform a posture calibration of the acceleration signalof the accelerometer.

In Example 29, the device of one or any combination of Examples 25-28optionally comprises the signal processor being configured to determinea consistency of the first or second gravity vector orientations duringthe first time period by: determining a central tendency or a dispersionof the gravity vectors; and determining whether the gravity vectors areconsistent using a comparison of the dispersion to a specifieddispersion threshold value.

In Example 30, the device of one or any combination of Examples 25-29optionally comprises the signal processor configured to identify aposture during the first or second time periods.

In Example 31, the device of one or any combination of Examples 25-30optionally comprises the signal processor being configured to identify aposture during the first or second time periods using at least one of: atime of day; a patient activity level; and a physiologic sensor, of adifferent type than the accelerometer.

In Example 32, the device of one or any combination of Examples 25-31optionally comprises the signal processor configured to perform theposture calibration, when the first or second gravity vectororientations are deemed inconsistent, without using the inconsistentgravity vectors.

This overview is intended to provide an overview of subject matter ofthe present patent application. It is not intended to provide anexclusive or exhaustive explanation of the invention. The detaileddescription is included to provide further information about the presentpatent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralscan describe substantially similar components throughout the severalviews. Like numerals having different letter suffixes can representdifferent instances of substantially similar components. The drawingsillustrate generally, by way of example, but not by way of limitation,various embodiments discussed in the present document.

FIG. 1 is schematic diagram illustrating generally an example of acardiac function management system, such as for use with a human oranimal subject.

FIG. 2 is a set of diagrams illustrating generally an example of a threeaxis accelerometer system.

FIG. 3 is a block diagram illustrating generally an example of a systemfor automatically calibrating a posture sensor device by detecting awalking state.

FIG. 4 is a flow chart illustrating an overview of an example of amethod for performing posture calibration when a patient is in a walkingstate.

FIG. 5 is a flow chart illustrating an example of a method for detectinga walking state.

FIG. 6 is a flow chart illustrating an overview of an example of amethod for performing posture calibration when a posture change isdetected in a patient.

FIG. 7 is a flow chart illustrating an example of a method for detectinga posture change.

DETAILED DESCRIPTION

This document describes, among other things, a system and method forautomatically calibrating a posture sensor, such as by detecting awalking state or a posture change. Detecting such a state or posture,along with calculating a corresponding gravity vector, can be used tocalibrate subsequent posture data.

FIG. 1 is schematic diagram illustrating generally an example of acardiac function management system 100, such as for use with a humansubject 101. In this example, the system 100 includes an implantable orexternal cardiac function management device 102. The implantable orexternal cardiac function management device 102 can include or becommunicatively coupled to an implantable or external posture-sensingaccelerometer or other implantable or external posture sensing device.The cardiac function management device 102 can include a communicationcircuit, such as for establishing a bidirectional wireless communicationlink 104 with an external local interface 106, an implantable orexternal posture sensor, or other device with communication capability.In certain examples, the external local interface 106 can furtherbidirectionally communicate with an external remote interface 108,wirelessly or otherwise, such as via a shared communication or computernetwork 110.

FIG. 2 is a set of diagrams illustrating generally an example of a threeaxis accelerometer system 200 located in three dimensional (3D) spacethat can be represented using an x-axis 202, a y-axis 204, and a z-axis206. These axes can be oriented such that the force of gravity can berepresented by a vector that is in the −z direction in the 3D space,opposite to the z-axis 206. The three axis accelerometer 200 can be usedto output an acceleration signal that includes (1) an activity componentindicative of a patient's physical activity; and (2) a posture componentindicative of a patient's posture. In certain examples, it may be usefulto calibrate the posture component of the three-axis accelerometersignal, such as by using information obtained in a particularascertainable posture (e.g., standing, sitting, supine, left decubitus,right decubitus, etc.).

In an illustrative example of such calibration, accelerometer signalinformation can initially be used to hypothesize that a patient is in a“standing” posture, and then the accelerometer signal information in thestanding posture can be used for automatically calibrating subsequentposture or other accelerometer information. Electrically transducedacceleration information (x, y, z) can be obtained from the x-axis 208,y-axis 210, and z-axis 212 electrical signal outputs of the three axisaccelerometer 200, such as when the patient is in the hypothesizedstanding posture, over a first sampling time period. An average (orother central tendency) of the acceleration can be calculated for eachof the x-axis 208, y-axis 210, and z-axis 212 over the first samplingtime period, yielding a vector 214 (x1, y1, z1) that is indicative ofthe direction of gravity in the standing posture during the firstsampling period. If the accelerometer were implanted perfectly upright,and the patient were standing perfectly upright, the vector 212 wouldhave values (0, 0, z1=−g), where g=magnitude of acceleration of gravity.However, since the accelerometer may not have been implanted perfectlyupright, and since the patient may not be standing perfectly upright,the vector 212 (x1, y1, and z1) will be different from (0, 0, z1=−g). Acoordinate rotation vector can be calculated to represent the particularorientation of the accelerometer in the standing posture during thefirst sampling time period (see, e.g., Hua Wang and John Hatlestad U.S.patent application Ser. No. 11/283,490 (U.S. Patent App. Publication No.20070118056, now U.S. Pat. No. 8,366,641, which is incorporated hereinby reference in its entirety). The coordinate rotation vector can beused to calibrate subsequent posture data, so that such subsequentposture data can always be expressed relative to the orientation of thebody.

Since there may be some movement or other postural variations in thestanding posture, it can be useful to sample over one or more furthersampling periods, such as a second sampling period, or over a longerfirst sampling period. In certain examples, a vector (x2, y2, z2) iscalculated over the second sampling period, or other further samplingperiods, and a mean or other central tendency of the two vectors (x1,y1, z1) and (x2, y2, z2) can be computed over the various samplingperiods before calculating a coordinate rotation vector between the meanor central tendency of the various sampled vectors and (0, 0, z1=−g).

FIG. 3 is a block diagram illustrating generally an example of a system300 that can be used for automatically calibrating a posture sensordevice 302, such as by detecting a walking state. The external orimplantable posture sensor device 302 can be included in orcommunicatively coupled to an implantable or an external cardiacfunction management device 102. In this example, a three-axisaccelerometer 304 can be configured to transduce an acceleration signalfrom a patient into an electrical or other signal that includesacceleration or posture information. The accelerometer 304 can becoupled to a signal processor circuit 305, which can include or becoupled to a low-pass filter circuit 315. The low-pass filter circuit315 can be configured to compute a substantially DC component of theacceleration signal, such as when the detected acceleration signal isconsistent with walking (e.g., walking is hypothesized). The signalprocessor circuit 305 can use information from the low-pass filtercircuit 315 to determine a gravity vector using the DC components of theacceleration signal along the x-axis 208, y-axis 210, and z-axis 212 ofthe accelerometer. In order to determine the consistency of one or moresuch gravity vectors obtained in the walking state, the signal processorcircuit 305 can determine a central tendency of the gravity vectors andcan compare the central tendency to a previously-determined centraltendency of the gravity vectors. A resulting difference between thebetween the central tendency of the gravity vectors and thepreviously-determined central tendency of the gravity vectors can beused to determine the consistency of the gravity vectors. In certainexamples, when the gravity vectors are deemed consistent, the centraltendency of the consistent gravity vectors can then be used to perform acalibration of the accelerometer.

In certain examples, once the gravity vectors are deemed consistent, thesignal processor circuit 305 can compute (1) a parallel component of theacceleration signal in the direction of the gravity vector and (2) anorthogonal component of the acceleration signal in a directionorthogonal to the gravity vector. The signal processor circuit 305 canthen compare the parallel component to a parallel template and theorthogonal component to an orthogonal template. Using the computedparallel and orthogonal components of the acceleration signal, thesignal processor circuit 305 can further distinguish the walking statefrom the non-walking activity state.

Posture calibration can involve using a walking state detection circuit306, which can be coupled to, or included within, the signal processorcircuit 305. The walking state detection circuit 306 can be configuredto detect a walking state of a patient using the acceleration signal.The waking state detection circuit 306 can include or be coupled to ahighpass filter circuit 308, a stored template 310, a comparison circuit312, and a posture calibration circuit 314. In certain examples, thehighpass filter circuit 308 can be configured to extract an AC componentof the acceleration signal, from which the signal processor circuit 305can extract a characteristic of the AC component of the accelerationsignal. The characteristic of the AC component extracted can include afrequency-domain characteristic, a time-domain characteristic, or atime-frequency-domain characteristic. In certain examples, one or moresuch characteristics can be separately extracted for the parallelcomponent of the acceleration signal and the orthogonal component of theacceleration signal. Thus, examples of possible characteristics of an ACcomponent extracted can include a parallel component frequency-domaincharacteristic, a parallel component time-domain characteristic, aparallel component time-frequency-domain characteristic, an orthogonalcomponent frequency-domain characteristic, an orthogonal componenttime-domain characteristic, or an orthogonal componenttime-frequency-domain characteristic. One or more such extracted ACcomponent characteristics can be compared to a corresponding storedtemplate 310. The stored template 310 can include, in certain examples,one or more of a parallel component frequency-domain template, aparallel component time-domain template, a parallel componenttime-frequency-domain template, an orthogonal component frequency-domaintemplate, an orthogonal component time-domain template, or an orthogonalcomponent time-frequency-domain template. The comparison circuit 312 canbe configured to perform a correlation or other comparison between asignal characteristic and its corresponding template. Using the resultof the comparison, the signal processor circuit 305 can distinguish thewalking state from a non-walking activity state. Information from thecomparison circuit 312 can be communicated to the posture calibrationcircuit 314, which can perform posture calibration of the accelerationsignal, such as described in Hua Wang and John Hatlestad U.S. patentapplication Ser. No. 11/283,490 (U.S. Pat. App. Publication No.20070118056), now U.S. Pat. No. 8,366,641, which is incorporated hereinby reference in its entirety, and Hua Wang et al. U.S. patentapplication Ser. No. 11/283,489 (U.S. Pat. App. Publication No.20070115277) now U.S. Pat. No. 7,471,290, which is also incorporatedherein by reference in its entirety. The posture calibration informationcan then be communicated to the communication circuit 316, such as forcommunicating with the local interface 106.

FIG. 4 is a flow chart illustrating an overview of an example of amethod for performing posture calibration when a patient is in a walkingstate. At 402, an acceleration signal can be sensed from a patient, suchas by using a three-axis accelerometer. The accelerometer can beimplantable or external. At 404, a walking state can be detected usingthe acceleration signal. At 406, the orientation of the accelerometer inthe walking state can be used to perform a posture calibration.

FIG. 5 is a flow chart illustrating an example of a method for detectinga walking state. At 502, an acceleration signal can be sensed from apatient, such as by using an implanted or external three-axisaccelerometer. At 504, the AC component of the acceleration signal canbe evaluated to determine whether it is consistent with walking orinconsistent with walking. In certain examples, this can includecomparing an amplitude of the AC acceleration signal to a minimumamplitude threshold, such that if the AC amplitude exceeds the minimumamplitude threshold, walking is hypothesized. In certain examples, thiscan also include comparing the amplitude of the AC acceleration signalto a maximum amplitude threshold, such that walking is hypothesized whenthe AC amplitude exceeds the minimum amplitude threshold and is alsobelow the maximum amplitude threshold. These comparisons can be madeusing a lumped vector sum magnitude of the individual 3D outputs of the3D accelerometer, or can be made by comparing one or more individual 3Doutputs of the 3D accelerometer to individual respective thresholdvalues, and constructing a rule for combining the results of the threecomparisons (e.g., x, y, z amplitudes all exceed their respectivethreshold values, 2 of 3 of the x, y, or z amplitudes exceed theirrespective threshold values etc.). In certain examples, a particular one(or a specified combination) of the x, y, or z outputs is used for thecomparison. In certain examples, the amplitude comparison can be madeusing accelerometer signal amplitude in anterior or posteriordirections, instead of other directions, such as to avoid possible noisedue to accelerations in different directions (e.g., up-and-downacceleration due to riding in a car, elevator, etc.).

If the AC component of the acceleration signal is determined to beinconsistent with walking, at 506, a walking state is not declared. Ifat 504 the AC component of the acceleration signal is determined to beconsistent with walking, a walking state is hypothesized at 508. Inaddition or alternative to the AC component of the acceleration signal,one or more other optional inputs can be used to hypothesize walking,such as a time of day consistent with walking, or a physiological stateconsistent with walking (see, e.g., John D. Hatlestad et al, U.S. patentapplication Ser. No. 11/466,925 (U.S. Pat. App. Publication No.20080082001, now U.S. Pat. No. 8,343,049, which is incorporated hereinby reference in its entirety). Examples of a time of day consistent withwalking can include times when the patient is awake, such as duringdaytime hours (e.g. between 05:00 and 24:00). Other examples of a timeof day consistent with walking can include times of the day during whichwalking was previously detected. Examples of a physiological stateconsistent with walking can include one or more of a detected heartrate, respiration rate, or blood pressure, the detected value beingabove a specified threshold. In a hypothesized walking state, a gravityvector can be computed from the DC component of the acceleration signal508 in each of orthogonal x, y, and z directions of the accelerometer.At 510, a parallel component of the AC component of the accelerationsignal (e.g., parallel to the direction of the gravity vector) can becomputed. The parallel AC component can then be compared to a parallelAC template. At 512, an orthogonal component of the AC component of theacceleration signal (e.g., in a direction orthogonal to the gravityvector) can be computed. In an illustrative example, the orthogonalcomponent can be computed by forming, in the plane that is orthogonal tothe gravity vector, a set of two vectors that are orthogonal to eachother and to the gravity vector. A vector sum of the AC component of theacceleration signal in the plane that is orthogonal to the gravityvector can be used as the orthogonal component of the AC component ofthe acceleration signal for comparing to a corresponding orthogonal ACtemplate.

The orthogonal AC component can then be compared to an orthogonal ACtemplate. The comparisons between the parallel AC component and theparallel AC template, and between the orthogonal AC component and theorthogonal AC template, can be used to distinguish the walking statefrom a non-walking activity. At 514, one or more characteristics of theorthogonal or parallel AC components can be extracted. Examples ofcharacteristics of AC components include one or more frequency-domaincharacteristics, time-domain characteristics, and time-frequency-domaincharacteristics.

In a frequency domain example, a dominant signal amplitude of the(parallel or perpendicular component of the) AC signal can be extractedand its corresponding dominant signal frequency can be compared to acorresponding dominant signal frequency value of a template, andsimilarly, one or more higher harmonic frequencies of the dominantsignal frequency can be compared to corresponding harmonic frequencyvalues of the template.

In a time-domain example, a signal amplitude of the (parallel orperpendicular component of the) AC signal can similarly be extracted andcompared to an amplitude of template. In another example, the shape of(parallel or perpendicular component of the) AC signal can be comparedto a corresponding template, such as by performing a correlation betweenthe signal and the template over a single or multiple periods, orbetween selected fiducial features of the signal and correspondingfiducial features of the template, or the like.

In a time-frequency domain example, a change in a frequency domaincharacteristic (e.g., dominant signal frequency) over time can becompared to a template value.

These frequency-domain characteristics, time-domain characteristics, andtime-frequency-domain characteristics can be extracted for both theparallel component of the acceleration signal and the orthogonalcomponent of the acceleration signal. Therefore, examples of suchcharacteristics can include one or more of a parallel componentfrequency-domain characteristic, a parallel component time-domaincharacteristic, a parallel component time-frequency-domaincharacteristic, an orthogonal component frequency-domain characteristic,an orthogonal component time-domain characteristic, or an orthogonalcomponent time-frequency-domain characteristic. At 516, one or more ofsuch characteristics can be correlated or otherwise compared to one ormore corresponding walking templates 518. The walking templates 518 canbe patient-specific, or they can be generalized templates for use acrossa population or other plurality of patients. If the characteristics donot match the one or more walking templates, then walking is notdeclared at 520. If the characteristics match the one or more walkingtemplates, then walking can be declared 522.

FIG. 6 is a flow chart illustrating an overview of an example of amethod for performing posture calibration, such as when a posture changeis detected in a patient. At 602, an acceleration or otherposture-information including signal can be sensed from a patient, suchas by using a three-axis accelerometer. At 604, one or more of aspecified posture change can be detected, such as by looking for aspecified change in the orientation of the DC component of theacceleration signal. At 606, a plurality of gravity vectors can becalculated, such as by using the gravity vectors detected from theacceleration signal during a time period that is before the specifiedposture change, or by using the gravity vectors detected from theacceleration signal during a time period that is after the specifiedposture change. A consistency of the gravity vectors during thespecified time period, either before or after the posture change, can bedetermined. When the gravity vectors are deemed consistent, a centraltendency of the gravity vectors during the specified time period, eitherbefore or after the posture change, can be used to perform a posturecalibration at 608.

FIG. 7 is a flow chart illustrating an example of a method for detectinga posture change. At 702, the DC component of an acceleration signalfrom a patient can be computed, such as in each of orthogonal x, y, andz directions of a three-axis accelerometer. The DC component of theacceleration signal can be used to determine a gravity vector of theacceleration signal. At 704, a change in the orientation of the gravityvector of the acceleration signal is sought. If the angle change exceedsspecified threshold value, a posture change can be declared at 708. Ifthe angle change is not above the specified threshold value, no posturechange is declared at 706. One or more other inputs at 712 canadditionally or alternatively be optionally used to determine whetherthere has been a posture change. Examples of such additional inputs at712 can include, by way of example, but not by way of limitation, a timeof day consistent with a posture change, a physical activity levelconsistent with a posture change, or a physiological measurementconsistent with a posture change (see, e.g., John D. Hatlestad et al,U.S. patent application Ser. No. 11/466,925 (U.S. Pat. App. PublicationNo. 20080082001), now U.S. Pat. No. 8,343,049, which is incorporatedherein by reference in its entirety). For example, in the morning, whena patient gets up out of bed, the patient typically moves from a supineposition to a sitting position, and then possibly to a standingposition. These posture changes are typically accompanied by predictablechanges in physical activity level, and can be accompanied bypredictable changes in other physiological measurements, such as heartrate, respiration rate or blood pressure.

Once a posture change is declared at 708, a posture A and a posture Bcan be identified at 710. Posture A denotes the posture of the patientimmediately before the posture change, and posture B denotes the postureof the patient immediately after the posture change. At 714, an averageor other central tendency of a plurality of gravity vectors for postureA can be calculated. At 716, an average or other central tendency of aplurality of gravity vectors for posture B can be calculated. At 718, asingle data vector for a gravity vector for posture A can be included inthe group of posture A vectors, and a single data vector for a gravityvector for posture B can be included in the group of posture B vectors.The resulting groups of posture vectors can then be compared to aspecified threshold number to determine whether the group is deemedcomplete or incomplete at 720. If the resulting groups of posturevectors are declared incomplete, then more data vectors are obtained forinclusion in the group. If the resulting groups of posture vectors aredeclared complete, the central tendencies (e.g. centroid orientation) ofeach group of postures vectors can be calculated at 722, such as todetermine the consistency of the vectors within each group.

Determining the consistency of the gravity vectors for each posture, Aand B, can include determining a central tendency of the gravityvectors, comparing the central tendency of the gravity vectors to apreviously-determined central tendency of the gravity vectors, anddetermining whether the gravity vectors are consistent using adifference between the central tendency of the gravity vectors and thepreviously-determined central tendency of the gravity vectors. When thedifference between the central tendency of the gravity vectors and thepreviously-determined central tendency of the gravity vectors exceeds athreshold value, the gravity vectors are deemed inconsistent. At 724,the consistency of the gravity vectors for each of posture A and postureB can be evaluated by determining the dispersion of the specified groupof postures and comparing the dispersion to a specified dispersionthreshold value. In some examples the dispersion may be computed using apreviously-determined dispersion of the gravity vectors. At 726, thecomparison of the dispersion of the specified group of postures to thespecified dispersion threshold value can be used to determine whetherthe gravity vectors for the respective postures are consistent. A groupof gravity vectors with a consistency above a specified threshold valueis determined to be consistent 728. When a group of gravity vectors isdeemed consistent, a central tendency measurement (e.g. centroid angles)of the consistent gravity vectors is used to perform a posturecalibration of the acceleration signal of the accelerometer. When agroup of gravity vectors is not deemed consistent 730, an error countercan be incremented. If the counter indicates that more than N_(E)consecutive errors have occurred, an alert can be generated andcommunicated to a local or remote external user interface, so as toalert a user that manual calibration should be performed, instead of theautomatic calibration described herein.

ADDITIONAL NOTES

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” All publications, patents, and patent documentsreferred to in this document are incorporated by reference herein intheir entirety, as though individually incorporated by reference. In theevent of inconsistent usages between this document and those documentsso incorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, the code may be tangibly stored on one ormore volatile or non-volatile computer-readable media during executionor at other times. These computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAM's), read onlymemories (ROM's), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A device comprising: an accelerometer configured to sense an acceleration signal from a patient; a filter circuit configured to extract a characteristic of the acceleration signal; a comparison circuit configured to perform a comparison between the characteristic of the acceleration signal and a stored template to distinguish a walking state of the patient from a non-walking activity state of the patient; and a posture calibration circuit configured to use an orientation of the accelerometer in the walking state to perform a posture calibration of the acceleration signal of the accelerometer.
 2. The device of claim 1, wherein the filter circuit includes a highpass filter circuit configured to extract an AC component of the acceleration signal, the characteristic of the acceleration signal including a characteristic of the AC component of the acceleration signal.
 3. The device of claim 1, comprising a communication circuit configured to: receive posture calibration information from the posture calibration circuit; and communicate the posture calibration information to an external local interface.
 4. The device of claim 1, wherein the stored template includes a stored domain template, the domain being at least one of frequency, time, and time-frequency.
 5. The device of claim 4, wherein the comparison circuit is configured to perform a comparison between the characteristic and the stored domain template, wherein the characteristic comprises a domain characteristic.
 6. The device of claim 1, wherein the filter circuit includes a lowpass filter circuit configured to extract a DC component of the acceleration signal.
 7. The device of claim 6, comprising a signal processor circuit configured to: determine a gravity vector using the DC component of the acceleration signal; compute a parallel component of the acceleration signal, the parallel component being in the direction of the gravity vector; compute an orthogonal component of the acceleration signal, the orthogonal component being in a direction orthogonal to the gravity vector; compare the parallel component to a parallel template and compare the orthogonal component to an orthogonal template; and distinguish the walking state from the non-walking activity state using the computed parallel component of the acceleration signal and using the computed orthogonal component of the acceleration signal.
 8. The device of claim 7, wherein the signal processor circuit configured to distinguish the walking state from the non-walking activity state includes: determining a central tendency of the gravity vectors; comparing the central tendency of the gravity vectors to a previously-determined central tendency of the gravity vectors; and determining whether the gravity vectors are consistent using a difference between the central tendency of the gravity vectors and the previously-determined central tendency of the gravity vectors.
 9. A device comprising: an accelerometer configured to sense an acceleration signal from a patient; a first filter circuit configured to extract a first component of the acceleration signal; a comparison circuit configured to perform a comparison between a characteristic of the first component of the acceleration signal and a stored template to distinguish a walking state of the patient from a non-walking activity state of the patient; a second filter circuit configured to extract a second component of the acceleration signal; a signal processor circuit configured to: determine a gravity vector using the second component of the acceleration signal; compute a parallel component of the acceleration signal, the parallel component being in the direction of the gravity vector; compute an orthogonal component of the acceleration signal, the orthogonal component being in a direction orthogonal to the gravity vector; compare the parallel component to a parallel template and compare the orthogonal component to an orthogonal template; and distinguish the walking state from the non-walking activity state using the computed parallel component of the acceleration signal and using the computed orthogonal component of the acceleration signal; and a posture calibration circuit configured to use an orientation of the accelerometer in the walking state to perform a posture calibration of the acceleration signal of the accelerometer.
 10. The device of claim 9, wherein the first filter circuit includes a highpass filter circuit, the first component including an AC component of the acceleration signal.
 11. The device of claim 9, wherein the second filter circuit includes a lowpass filter circuit, the second component including a DC component of the acceleration signal.
 12. The device of claim 9, wherein the signal processor circuit configured to distinguish the walking state from the non-walking activity state includes: determining a central tendency of the gravity vectors; comparing the central tendency of the gravity vectors to a previously-determined central tendency of the gravity vectors; and determining whether the gravity vectors are consistent using a difference between the central tendency of the gravity vectors and the previously-determined central tendency of the gravity vectors.
 13. The device of claim 9, comprising a communication circuit configured to: receive posture calibration information from the posture calibration circuit; and communicate the posture calibration information to an external local interface.
 14. A system comprising: a device comprising: an accelerometer configured to sense an acceleration signal from a patient; a filter circuit configured to extract a characteristic of the acceleration signal; a comparison circuit configured to perform a comparison between the characteristic of the acceleration signal and a stored template to distinguish a walking state of the patient from a non-walking activity state of the patient; a posture calibration circuit configured to use an orientation of the accelerometer in the walking state to perform a posture calibration of the acceleration signal of the accelerometer; and a communication circuit configured to receive posture calibration information from the posture calibration circuit; and an external local interface configured to receive the posture calibration information from the communication circuit.
 15. The system of claim 14, wherein the device includes an implantable cardiac function management device.
 16. The system of claim 14, wherein the filter circuit includes a highpass filter circuit configured to extract an AC component of the acceleration signal, the characteristic of the acceleration signal including a characteristic of the AC component of the acceleration signal.
 17. The system of claim 14, wherein the external local interface is configured to wirelessly bidirectionally communicate with the communication circuit.
 18. The system of claim 14, comprising an external remote interface configured to bidirectionally communicate with the external local interface.
 19. The system of claim 14, wherein the filter circuit includes a lowpass filter circuit configured to extract a DC component of the acceleration signal.
 20. The system of claim 19, wherein the device includes a signal processor circuit configured to: determine a gravity vector using the DC component of the acceleration signal; compute a parallel component of the acceleration signal, the parallel component being in the direction of the gravity vector; compute an orthogonal component of the acceleration signal, the orthogonal component being in a direction orthogonal to the gravity vector; compare the parallel component to a parallel template and compare the orthogonal component to an orthogonal template; and distinguish the walking state from the non-walking activity state using the computed parallel component of the acceleration signal and using the computed orthogonal component of the acceleration signal. 